Volunteers collaborate with professionals to provide institutional assistance in various areas; their management differs from that of professionals. To maximise volunteer productivity, categorising volunteers according to their knowledge and experience levels and distributing tasks increases volunteer retention and task efficiency. This study categorizes volunteers into four groups, fulfilling nine criteria identified through a literature review of search and rescue activities. TOPSIS-Sort and ORESTE-Sort (Optimist and Pessimist approaches) are employed in classification. Only one alternative is consistently assigned to the same class across all methods. While ORESTE-Sort offers greater flexibility and three distinct classification outcomes, it is more complex and sensitive to threshold values. TOPSIS-Sort, though simpler and producing a single classification, requires criterion weight. The Wilcoxon signed rank test is applied to evaluate the similarity between ORESTE-Sort and TOPSIS-Sort results; there are significant differences between the two methods. The study concludes by comparing the methods' applications, steps, and solution approaches, highlighting their respective advantages and disadvantages, and suggesting directions for future research.
The Internet of Things (IoT) is a rapidly growing technology that connects devices and enables data exchange, allowing for advanced automation and optimization in hospitals. Despite its potential benefits, IoT adoption faces several challenges that need to be addressed. In this context, we reviewed the existing literature on IoT adoption challenges and identified the most critical factors that affect the adoption of IoT. Furthermore, thes Analytic Hierarchy Process (AHP) is applied to determine the comparative weight of each challenge. Additionally, a comparative analysis of two novel Multi-Criteria Decision Making (MCDM) techniques, namely Weighted Aggregates Sum Product Assessment (WASPAS) and Combined Compromise Solution (CoCoSo) are applied to evaluate the rank of all the alternatives in the Pythagorean fuzzy environment. Furthermore, the sensitivity analysis was conducted to check the model’s suitability and reliability in a scientific manner. The results of the evaluation indicate that the most critical challenges in the adoption of IoT devices in hospitals are privacy and security of patient data, IT infrastructure, top management support, partner collaboration and implementation cost. The findings can help policymakers, industry practitioners and other stakeholders to make informed decisions in their efforts to enhance the adoption and deployment of IoT technology.
In the digital age of education, e-learning has become an integral part of the learning process. However, decision-making processes in this field involve elements of uncertainty and hesitation that need to be managed effectively. This study addresses Multi-Criteria Decision Making (MADM) problems using Trapezoidal Hesitant Fuzzy Numbers (THIFN), a powerful tool for representing uncertain and hesitant information. Two new operators are proposed for situations where feature prioritization is required: THIFN-Prioritized Weighted Average (THIFNPWA) and THIFN-Prioritized Weighted Geometric (THIFNPWG). The fundamental properties of the proposed operators, such as uniformity, boundedness, and monotonicity, are theoretically examined. The developed approach is applied to a real case study for selecting the most suitable e-learning platform. A comprehensive comparative analysis is conducted with existing methods in the literature to prove the validity of the methodology. The analysis results showed that the proposed method accurately preserved priority relationships between features and produced more consistent and discriminatory ranking results compared to existing approaches. Findings from the comparative analysis demonstrate that this method exhibits superior performance in processing prioritized and ambiguous data and offers a reliable information fusion model for hesitant decision-making environments.
Various set theories have been developed to model the uncertainties frequently encountered in real-world scenarios. This study proposes an entropy-based VIKOR method based on Generalized Trapezoidal Fuzzy Numbers (GTHFNs) due to their high representation capacity, with the aim of addressing uncertainty more effectively. Entropy is used to express the mathematical values of the fuzziness of GTHFNs. To ensure flexibility, the proposed method has been formulated using a Minkowski-type distance measure. This decision-making framework not only provides a way to solve the MCDM problem but also incorporates an important mathematical idea as a different solution approach. The applicability of the proposed algorithm is demonstrated through a numerical case study. Comparative results show that the method provides a more precise and effective distinction among alternatives, proving its validity in environments characterized by high uncertainty and incomplete information.
Geometric programming (GP) is a well-established optimization framework widely used in engineering design and related areas for solving nonlinear optimization problems. Classical Classi approaches for solving GP problems typically rely on dual formulations, which may become restrictive when the degree of difficulty is high. In this paper, we propose a direct primal approach for solving constrained posynomial geometric programming problems by reformulating them as nonlinear fractional programming problems, without resorting to duality. The proposed method transforms the original GP into a ratio optimization problem and applies a parametrization technique based on the Dinkelbach method to obtain the optimal solution. This approach allows GP problems with nonzero degrees of difficulty to be handled in a systematic and computationally efficient manner. Theoretical results supporting the proposed formulation are presented, with several lemmas developed within the paper and standard results clearly distinguished from those recalled from existing literature. Numerical examples are provided to demonstrate the effectiveness and accuracy of the proposed approach. In addition, potential challenges associated with large-scale geometric programming problems, such as computational complexity and convergence issues, are briefly discussed.
In this paper, we study a two-stage chain-reentrant hybrid flow shop with deteriorating jobs as follows. Each job must initially be scheduled on a primary machine M1 (first stage) which is then scheduled on one of a set of m unrelated parallel machines (second stage) and returns back to M1 for its last operation. The jobs are subjected to a linear deterioration function of their starting times. The aim is to minimize both of the makespan and the total energy consumption. For the resolution of this problem, we have developed a mixed-integer linear programming model. We then implement and compare two metaheuristics: a nondominated sorting-based multiobjective genetic algorithm (NSGA2) and an archived multiobjective simulated annealing algorithm (AMOSA). The experimental study indicates that AMOSA generally outperforms NSGA2 on small instances (for all tested values of m and n ? 30), whereas NSGA2 yields better performance on larger instances (for all tested values of m and n ? 100), with respect to both quantity and quality measures. For small instances, the comparison with the exact method (i.e., the mathematical model) confirms that the reference fronts generated by both algorithms are close to the true Pareto front. Finally, we have proposed a TOPSIS-based method to select a representative solution from the Pareto set according to the decision-maker?s preferences.
In this paper, we point out a fundamental error in the procedures used to measure ballistic dispersion. We argue that there are two components to a proper measure of dispersion: i) dispersion arising from the ?unknown? aim point and ii) dispersion caused by the cloud, which can be measured directly from the sample mean point of impact. The procedures currently in use only take into account the cloud dispersion, and thus, underestimate the actual (total) dispersion. These procedures fail to recognize the existence of the fixed ?aim point?. As a result, incorrect conclusions may be drawn. An elementary correction to the underlying statistics corrects this error when the fall of shot follows a circular normal distribution.
This article investigates the optimal control problem of a Car-Like mobile robot, with our primary objective being the identification of the optimal control strategy that facilitates the transition from an initial point to an endpoint in the shortest possible time. To address this challenge, we employ Pontryagin’s Maximum Principle, coupling it with the shooting method to determine the initial condition of the adjoint state (p0), which is then integrated through the iterative Picard method. The validation of our approach is demonstrated through a numerical example utilizing real-world data, and we provide a comparative a comparative analysis of our results against those from previous studies for comprehensive assessment.
The primary objective of this research is to develop more reliable portfolios by accurately calculating risk and return, emphasizing a secure asset weighting strategy. We employ the DEA bootstrap method and the SPP-CVaR (Stop-Profit Point-Conditional Value at Risk) methodology to achieve this objective. Previous scholarly research often lacks a robust statistical foundation for evaluating asset performance, particularly regarding sustainability, as traditional approaches rely on single data samples. Additionally, many studies fail to account for the relevance of risk and return until the investor exits the market. We introduce a new approach focusing on exit time to evaluate sustainable investments to address this gap. We employ data envelopment analysis (DEA) to assess the performance of these assets, comparing results from both the DEA bootstrap method and traditional DEA models. Our DEA models incorporate SPP-CVaR (Conditional Value at Risk) as a measure of risk and mean return as the output variable, both calculated until the investor exits the market. Traditional DEA models have limitations in statistical interpretation, so we enhance our analysis with the DEA bootstrap method. This method involves resampling data to create multiple samples, offering a distribution of performance measures for each asset and providing a more comprehensive understanding of asset performance and uncertainty. By comparing the bootstrap and results of conventional methods, we demonstrate the advantages of using statistical techniques to evaluate and compare financial assets. The SPP-CVaR is calculated by deriving and converting the risk-neutral density, simulating price paths, and identifying stop-profit points. We then analyze the exit time and price distributions to compute the SPP-CVaR for each stop-profit point. The value of this study lies in its integration of sustainability analysis with risk measures, helping investors build profitable and ethically aligned portfolios. By providing a detailed assessment of an asset's sustainability profile, our approach assists investors in making informed decisions that align with their financial and ethical goals.
This research implements the impact of an advance payment policy on inventory management while incorporating preservation technology to mitigate product deterioration. In a real scenario, the sum of the membership and non-membership degrees of uncertain parameters is greater than one. Hence, the primary objective is to optimize cycle time, selling price and maximum profit by utilizing an advanced payment policy, hybrid price-dependent demand rate under interval-valued Pythagorean fuzzy numbers to handle imprecise parameters. Two inventory models are developed: one incorporating preservation technology and another without it. Then, the corresponding fuzzy models are obtained under interval-valued Pythagorean fuzzy environment. A novel ranking method is employed to defuzzify the models and then the defuzzified models are solved by using analytic solution method of maximization problem. The models are validated through numerical examples by assuming hypothetical data, and the results are compared across crisp, Pythagorean fuzzy, and intuitionistic fuzzy frameworks. Key findings indicate that adopting preservation technology significantly improves profitability and reduces deterioration losses. Moreover, the Pythagorean fuzzy approach proves to be more effective in capturing uncertainty compared to intuitionistic fuzzy sets. These findings suggest that businesses can enhance inventory decision-making by leveraging advanced fuzzy techniques to optimize financial and operational outcomes.
Workplace interruptions have become increasingly prevalent due to the advancement of mobile communication technologies and changing work practices. Despite the significant impact of work interruptions, the ways in which individuals respond to these disruptions remain largely unexplored. This study, grounded in affective event theory, investigates the sources of workplace interruptions, specifically focusing on receiving phone calls, and examines how these interruptions contribute to work-life conflict through the mediating role of affectivity (positive and negative affect). Data were collected from 250 employees in the banking sector of Pakistan. The findings indicate that receiving calls is positively associated with work-life conflict and that affectivity (both positive and negative) mediates this relationship. These results offer valuable theoretical and practical insights for practitioners and decision-makers aiming to mitigate the negative impacts of workplace interruptions on employees' work-life balance.
The massive digitalization of supply chains has made information security and privacy of the utmost importance. Quantum cryptography (QC) technology has gained notable importance for data encryption to ensure these. This paper aims to fulfill two objectives: a) to unveil the barriers to the adaptation of QC in digital supply chains, and b) to develop innovative Comparisons between Ranked Criteria (COBRAC) framework using q-rung Orthopair fuzzy Einstein weighted averaging (q-ROFEWA) for multi-criteria decision-making (MCDM). The present work designs a group decision-making study based on the opinions of 12 experts, expressed in linguistic terms. To derive the barriers, the current work uses the theoretical framework of Technology-Organization-Environment (TOE). From the analysis, it is revealed that lack of awareness and knowledge (w = 0.1733), trust and privacy issues (w = 0.1624), and scale-up and infrastructural capability (w = 0.1348) are the top three barriers. It is seen that the model provides a robust result, maintaining a statistically significant high correlation with other MCDM methods. The sensitivity analysis demonstrates no considerable variation in the final result, given the changes in parameter values. The findings provide significant
COVID-19 continued to spread fast throughout the world since its outbreak from December, 2019. Most of the affected countries faced a huge challenge in managing the infection rate and providing the required treatments to the infected ones, which led the researchers to investigate the necessary causes and solutions regarding the infections. Researchers were also involved in estimating and forecasting the future trends and effects of COVID-19 as prediction is crucial to handling the unwanted pandemic situation. The uncertain nature of COVID-19 inspired researchers to adopt fuzzy sets for managing the pandemic. Researchers introduced various fuzzy logic-based models to analyze the pandemic situation and predict future directions. The aim of this study is to present an organized literature review to study the applicability of fuzzy set theory and its extensions in order to manage the pandemic situation. The COVID-19 related articles are grouped into six domains related to predictions (S1), related factor analysis (S2), prevention, control and managing the situation (S3), analysis of treatment (S4), after effects (S5), and distribution of vaccine (S6). Insights of the published articles are depicted using tabular representations. We have analyzed the significance of various categories to explore their societal impacts. This comprehensive review reveals a greater emphasis on experimenting with strategies to control the impact of COVID-19, while there is less focus on studying the effects of COVID-19, particularly in terms of vaccine distribution. The domain-wise data analysis from current research presents various approaches and directions. Additionally, this study predicts future research directions for each of the mentioned categories. Researchers initially focused on prevention, prediction, and control of COVID-19. The analysis reports illustrate the effects of COVID-19 factors and their social impacts on communities.
The agricultural industry is experiencing a transformative shift through the adoption of Internet-of-Things (IoT) technology, often termed as "smart agriculture." This paradigm shift is revolutionizing traditional farming practices, making them more precise, efficient, and data-driven. This study presents a novel contribution to the field by developing an intelligent water management system using IoT sensors, specifically designed for precision irrigation. Unlike existing systems, this solution enables real-time monitoring of critical environmental parameters such as soil moisture, humidity, and temperature with unparalleled accuracy. The unique contribution of this work lies in its data-driven approach to optimizing irrigation practices, which not only enhances water-use efficiency but also significantly improves crop yield and quality. Furthermore, the system's capability for remote monitoring and management minimizes the need for manual interventions, thereby reducing operational costs. The study demonstrates the practical application of IoT in mitigating water management issues and increasing agricultural productivity, particularly in the context of climate change challenges. This work provides a scalable and sustainable model for IoT adoption in agriculture, offering a robust framework for improving efficiency, sustainability, and resilience in farming practices.
This paper presents a robust algorithmic framework for evaluating and ranking world happiness using a novel model based on q-rung orthopair triangular fuzzy neutrosophic sets (PQ-RTFNs) in a possibility setting. Traditional methods of ranking countries by happiness often face challenges in handling the complexity and uncertainty of the various social, economic, and environmental factors that influence well-being. To address this, the authors integrate the flexible and powerful PQ-RTFN model, which allows for better representation of indeterminate and inconsistent data. This approach enhances decision-making accuracy by effectively managing the fuzziness and ambiguity inherent in world happiness metrics. Through a comprehensive evaluation, the proposed framework demonstrates improved performance in ranking nations compared to existing models, offering a more reliable tool for policymakers and researchers to assess global happiness indices.
Real-world decision making problems often dictates to take into account several point of view that are objectively conflictuel. Many studies were dedicated to provide decision makers with methods for solving this type of highly complex problems. In this paper, we propose a new hybrid multi-criteria decision making method with a new hybrid normalization and aggregation strategies. Mainly, the proposed method introduces a new hybrid normalization between the distance measure and the ratio system, and also uses two hybrid equations to compute the weighted performance of alternatives as to improve the stability of the method and the flexibility of the results. Moreover, hybrid aggregation rule based on exponential and logarithmic functions is proposed to establish the final ranking of the alternatives. To assess the performance of the proposed method, we used two real problems: the logistic provider selection problem and the evaluation of microclimate in an office problem. Comparative results with eight state-of-the-art multicriteria decision making methods and sensitivity analysis established its validity, in terms of performance and stability, for solving multi-criteria decision making problems.
Fuzzy parameterized neutrosophic soft expert sets is a useful extension of fuzzy parameterized intuitionistic fuzzy soft expert and neutrosophic soft sets. This set deals with attributes as well but with many definitive assessments, whereas the soft set only addresses one set of attributes. The development of this model addresses the limitations of existing soft set-based models, particularly their inability to handle multiple experts? opinions in a decision-making context. This paper?s major goal is to introduce the theory of fuzzy parameterized neutrosophic soft expert set theory. It also discusses the notion?s numerous related ideas and the basic operations on it, such as the complement, union, intersection, AND, and OR. The essential features of this idea are established, as are relevant rules such as De Morgan?s laws. A generalised algorithm is then used to apply the suggested idea of fuzzy parameterized neutrosophic soft expert sets in a decision making situation. Moreover, in order to check the validity of the proposed algorithm, an example related to the choice of the area of building project using the decision-making method is presented. The suggested method provides reliable, reliable decision-making outcomes, and its enhanced capacity and dependability are confirmed by a robust comparison with earlier models.
This study proposes an advanced inventory model for non-instantaneously deteriorating items, integrating preservation technology, carbon emissions considerations, price-dependent demand, and a hybrid payment scheme within a two-warehouse framework. The hybrid payment method, combining partial upfront and deferred payments, enhances cash flow flexibility, which is critical for managing financial constraints in supply chain operations. The model aims to optimize inventory management by minimizing total costs while fostering environmental sustainability. Key features include investments in green technology to reduce carbon emissions and mitigate item deterioration, along with dynamic pricing strategies to respond to market demand fluctuations. Numerical analyses validate the model, revealing that preservation technology investments significantly lower total costs by extending product shelf life, while effective carbon management reduces transportation expenses. The hybrid payment scheme also proves to be a strategic tool for balancing financial obligations and operational efficiency. Sensitivity analysis conducted using MATLAB R2024a highlights the impact of changes in key parameters, such as demand elasticity, deterioration rate, and carbon tax, on the total cost. The findings provide actionable insights for managers to enhance inventory efficiency and sustainability, particularly in cost-sensitive and environmentally regulated industries.
One application of lattices in optimization is defining the equilibrium set of ordered games, typically using the usual (coordinate-wise) order, which is incomplete in Rn. This incompleteness makes some strategies incomparable, requiring a special game concept. Using a complete order, like the lexicographic order, results in a complete lattice. This study explores the properties of a complete lattice with lexicographic order for noncooperative games and provides a Python algorithm to determine the Nash equilibrium of a supermodular game.
Unpredictability and uncertainty occur worldwide in various aspects of real life. We cannot predict some specific outcomes or events precisely due to multiple factors, randomness, complexity and limited information. These situations can be handled efficiently in a systematic way by using neutrosophic sets. In real-world applications, transportation is essential in all sorts of movement of goods, services and people to meet various needs and demands efficiently. This study concentrated on the multiple objectives, multiple choice transportation problem in interval-valued trapezoidal neutrosophic contexts. The conversion procedure employs a de-neutrosophication process that relies on interval numbers rather than crisp numbers. By using an interval-valued trapezoidal neutrosophic fuzzy programming method based on interval number, the identified uncertain transportation problem is then solved. Additionally, an illustrative instance is presented to showcase the successful implementation of the proposed methodology.
